Literature DB >> 32344461

Genetic variants and underlying mechanisms influencing variance heterogeneity in maize.

Hui Li1, Min Wang2, Weijun Li1, Linlin He1, Yuanyuan Zhou1, Jiantang Zhu1, Ronghui Che1, Marilyn L Warburton3, Xiaohong Yang2, Jianbing Yan4.   

Abstract

Traditional genetic studies focus on identifying genetic variants associated with the mean difference in a quantitative trait. Because genetic variants also influence phenotypic variation via heterogeneity, we conducted a variance-heterogeneity genome-wide association study to examine the contribution of variance heterogeneity to oil-related quantitative traits. We identified 79 unique variance-controlling single nucleotide polymorphisms (vSNPs) from the sequences of 77 candidate variance-heterogeneity genes for 21 oil-related traits using the Levene test (P < 1.0 × 10-5 ). About 30% of the candidate genes encode enzymes that work in lipid metabolic pathways, most of which define clear expression variance quantitative trait loci. Of the vSNPs specifically associated with the genetic variance heterogeneity of oil concentration, 89% can be explained by additional linked mean-effects genetic variants. Furthermore, we demonstrated that gene × gene interactions play important roles in the formation of variance heterogeneity for fatty acid compositional traits. The interaction pattern was validated for one gene pair (GRMZM2G035341 and GRMZM2G152328) using yeast two-hybrid and bimolecular fluorescent complementation analyses. Our findings have implications for uncovering the genetic basis of hidden additive genetic effects and epistatic interaction effects, and we indicate opportunities to stabilize efficient breeding and selection of high-oil maize (Zea mays L.).
© 2020 Society for Experimental Biology and John Wiley & Sons Ltd.

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Keywords:  gene × gene interactions; maize; mean-effect SNP; vGWAS; variance heterogeneity

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Year:  2020        PMID: 32344461     DOI: 10.1111/tpj.14786

Source DB:  PubMed          Journal:  Plant J        ISSN: 0960-7412            Impact factor:   6.417


  2 in total

1.  Assessment of two statistical approaches for variance genome-wide association studies in plants.

Authors:  Matthew D Murphy; Samuel B Fernandes; Gota Morota; Alexander E Lipka
Journal:  Heredity (Edinb)       Date:  2022-05-10       Impact factor: 3.832

2.  PEG-Delivered CRISPR-Cas9 Ribonucleoproteins System for Gene-Editing Screening of Maize Protoplasts.

Authors:  Rodrigo Ribeiro Arnt Sant'Ana; Clarissa Alves Caprestano; Rubens Onofre Nodari; Sarah Zanon Agapito-Tenfen
Journal:  Genes (Basel)       Date:  2020-09-02       Impact factor: 4.096

  2 in total

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